张龙信,
张晓丽,
胡永祥
湖南工业大学 计算机学院 株洲 412007
基金项目:国家自然科学基金(61702178)，湖南省自然科学基金(2018JJ4068)，湖南省教育厅科研项目(18C0499)
详细信息
作者简介:肖满生：男，1968年生，教授，主要研究方向为智能计算和智能信息处理
张龙信：男，1983年生，博士，讲师，研究方向为大数据与数据安全
张晓丽：女，1994年生，硕士，研究方向为智能信息处理
通讯作者:肖满生 xiaomansheng@tom.com
中图分类号:TN911.7; TP391计量
文章访问数:1252
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被引次数:0
出版历程
收稿日期:2019-08-06
修回日期:2020-02-19
网络出版日期:2020-03-14
刊出日期:2020-08-18
An Improved Fuzzy Clustering Method for Interval Uncertain Data
Mansheng XIAO,,Longxin ZHANG,
Xiaoli ZHANG,
Yongxiang HU
School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China
Funds:The National Natural Science Foundation of China (61702178), The Natural Science Foundation of Hunan Provierce (2018554068), The Research Project of Hunan Provincial Department of Education (18C0499)
摘要
摘要:针对区间型不确定数据的特点，该文提出一种改进的模糊C均值聚类算法(IU-IFCM)。首先对区间型数据进行特征变换，由p维特征映射成由2p维特征组成的实数据，然后考虑区间中值与区间大小关系，设计一种样本距离计算方法，通过模糊C均值实现对区间型样本聚类。理论分析与对比实验表明，该算法的划分系数(PC)及正确等级(CR)值比其它方法平均提高10%以上，表明有更好的聚类精度，对当前大数据环境下不确定数据的分类提供了一种新的解决方案。
关键词:区间型数据/
模糊C均值/
影响因子/
特征变换
Abstract:An Improved Fuzzy C-Means clustering algorithm (IU-IFCM) is proposed in this study in accordance with the characteristics of Interval Uncertain data. First, the interval data is transformed into real data composed of 2p dimension feature, which is mapped from that of p dimension feature. Second, a method for calculating sample distance, which realizes the interval sample clustering by fuzzy c-mean algorithm, is designed while considering the relationship between interval median value and interval size. Theoretical analysis and comparison experiments show that the presented algorithm surpaes the compared algorithms by more than 10% on average in terms of the Partition Coefficient (PC) and Correct Rank(CR) value. These results indicate that the algorithm presents in this study has better clustering accuracy and provides a new solution for the classification of uncertain data in current big data environments.
Key words:Interval data/
Fuzzy C-means/
Impact factor/
Feature transformation
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